Mike: Hello, everybody, and welcome to the Unlearning Lab with JAR Consulting Group. I am here joined by Kevin Wosmansky, president, owner, founder, CEO, and mailroom clerk of JAR Consulting Group. How are we doing today, Kevin?

Kevin: Good, Mike. You know, at some point, you’re going to run out of extra job titles for me. So, mailroom clerk? That’s true. I check the post office box from time to time, so okay. I’ll give you that one.

Mike: Fair enough. So I’ll never run out because, guess what? You taught me how to use AI.

Okay, so let’s get into what’s important here. Kevin, on our last episode, we were talking about Google reviews. In this day and age with AI, we need to talk about how important Google reviews are for a business now that more people are using AI and large language models.

The general consensus is probably, “Well, I don’t need that anymore if things are shifting this way.” This is going to be your favorite episode because that’s really my only question, and you get to do ninety percent of the talking.

How important are Google reviews for my business in this new AI world?

Kevin: The answer is: critical—more important than they used to be, but not for the reasons they used to be.

Traditional search engines, even six months ago, focused heavily on star ratings. Large language models, however, read the actual review text to judge brand sentiment, authenticity, and real-world capabilities.

When people ask how important Google reviews are, my answer is: more important than ever. Google reviews are now a primary data source for generative engine optimization. Large language models analyze review text to determine sentiment and establish brand trust.

When you think about how LLMs identify business attributes and verify operational recency, reviews play a huge role. Consumers use reviews to determine whether they trust a business. What’s interesting is that machines are doing the same thing.

Unlike traditional search engines that primarily measured something like a 4.2-star rating, AI reads every review. Google reviews have become a primary driver in helping large language models determine sentiment and make recommendations.

Mike: That makes a lot of sense. I bet you have some data that supports this.

Kevin: Well, Mike, yes I do.

The data that settles this debate is pretty compelling. Ninety-three percent of consumers read online reviews before making a purchase decision or hiring a business. Businesses with ratings above four stars typically earn seventy percent more clicks.

When we work with businesses, I don’t really care whether they’re at a 4.0 or a 4.9—just be at four stars or above. Once you’re down around 3.8 or 3.9, it’s time to work on improving that rating.

Most people read Google reviews before buying or hiring, and AI models are doing the same thing.

When you talk about how reviews directly impact Google rankings, factors include review quality, review quantity, review velocity—how quickly new reviews come in—and whether owners respond to reviews.

I can’t stress enough how important it is to respond to reviews. Not only do reviews matter, but owner engagement matters too.

Mike: That’s interesting because I remember how much you emphasized to business owners: respond, respond, respond. People want to see that you’re engaged.

What you’re saying now is that machines want to see it too.

Kevin: Exactly.

Responding to reviews matters for both positive and negative feedback.

With a negative review, you get an opportunity to tell your side of the story. Customers are going to read the response and decide whether they believe the reviewer or the business owner.

We’ve all seen fake reviews. I’ve seen competitors leave negative reviews about other businesses. That’s why it’s important to tell your side of the story.

Now machines are evaluating those responses too. They’re trying to determine whether a negative review appears legitimate or not.

When you respond to positive reviews, it shows you’re an engaged and responsive business. That matters to humans, and it matters to AI systems.

Mike: That’s pretty cool. Since strategy is so important, can you share what JAR Consulting Group recommends to clients?

Kevin: Absolutely.

At JAR Consulting Group, we tell every client that Google reviews aren’t just social proof for human customers anymore. They are authority signals that AI systems actively use to decide who they trust and recommend.

Google reviews have evolved dramatically. Years ago, some people looked at reviews. Today, ninety-three percent of people read reviews before making a buying or hiring decision.

That’s all the proof you need.

Within our business, and for our clients, we implement systems that consistently ask customers for reviews and recommendations.

Most businesses fail because they don’t have a process in place. They aren’t sending emails or text messages requesting reviews.

In fact, you’re about five times more likely to get someone to leave a review if you send a text message with a direct review link rather than an email.

Yet most businesses don’t do that.

One of the primary things we do at JAR Consulting Group is help businesses implement systems and processes that make it easy to ask for reviews.

Mike: Perfect. I think the takeaway here is that Google reviews are massively important.

Would you say they’re more important now, less important, or equally important compared to the past?

Kevin: I think they’re more important today.

In the past, reviews were mainly for prospects, customers, and consumers. They still serve that purpose, but now machines are reading every single review as well.

We’ve talked before about large language model sentiment training. AI systems are trying to determine how people feel about your business.

What better way to establish sentiment than by analyzing what your customers are saying?

So yes, I would absolutely say Google reviews are more important in today’s AI-driven world than ever before.

Mike: I agree.

When I’m on Amazon shopping for my Speedo for an upcoming cruise, I might read three or four reviews. The machine reads all of them.

Kevin: That’s probably the most important point for listeners.

A human might read five or ten reviews before making a decision. But imagine a business with one hundred reviews. AI reads every single one.

Not only that, it reads every review for every competitor in that industry.

That’s pretty mind-boggling when you think about it.

Mike: That really drives the point home.

Most people are probably thinking, “Yeah, I only read a couple of reviews.”

Kevin: Exactly.

And here’s another interesting thing. Most people start with the negative reviews. I know I do.

Mike: I do too.

Kevin: It’s human nature. We sort by lowest rating first because we want to know what’s wrong.

Then we look at the positive reviews.

The advantage AI has is that it reads everything. You might have two or three bad reviews and one hundred great ones.

Humans can get hung up on a single negative review. AI evaluates the full picture.

Mike: That’s a great point.

We now know reviews are massively important to businesses—perhaps even more important than before.

I’ve also noticed that when I read reviews, I rarely pay attention to the date. AI knows whether a review is four years old, but I usually don’t notice.

Kevin: That’s another excellent point.

AI evaluates both recency and relevance because it reads every review.

Businesses that have a strategy and systems in place to consistently generate new reviews will have a major advantage over competitors.

Some industries naturally get lots of reviews, like restaurants. Other industries, especially in the B2B space, are very different.

There are also industries such as healthcare where regulations like HIPAA create challenges around requesting reviews.

But regardless of the industry, you need a strategy and a process for consistently generating reviews.

Right now, reviews are one of the best sources for large language models to determine sentiment about your business.

Mike: Perfect. Thanks, Kevin.

This was a great discussion and really drove the point home. I don’t know what we’ll talk about next time. Maybe we’ll figure it out over a Pepsi or a Mountain Dew.

Kevin: Maybe we’ll talk about your cruise. You’re going to be gone for a week. We could discuss how Mike used AI on his big Caribbean cruise.

Mike: You know what? I bet I can come up with some ideas while I’m traveling around the world.

Kevin: That’ll be your assignment, Mike. While you’re floating around in the ocean, think about how AI can be used on a cruise ship or while on vacation. That could be a fun topic.

Mike: It is. And for a paid assignment, that’s pretty good.

I’ll see you next time and we’ll figure something out.

Kevin: Take care, bud. Have a good trip.

Mike: Thanks.